Quantifying Pharyngeal Residue Across the Adult Life Span: Normative Values by Age, Gender, and Swallow Task
Bibliographic record
Abstract
Purpose: We quantified pharyngeal residue using pixel-based methods in a normative data set, while examining influences of age, gender, and swallow task. Method: One hundred ninety-five healthy participants underwent a videofluoroscopic swallow study following the Modified Barium Swallow Impairment Profile (MBSImP) protocol. ImageJ was used to compute Normalized Residue Ratio Scale and the Analysis of Swallowing Physiology: Events, Kinematics and Timing (ASPEKT) pharyngeal residue measures. Reliability was established. Descriptive statistics were performed for all residue measures. Inferential statistics were performed using ASPEKT total scores (i.e., %C2–4 2 ). Logistic regression models explored predictors of residue versus no residue. Generalized linear mixed models explored predictors of nonzero residue. Spearman rho explored relationships between ASPEKT total residue scores and MBSImP Component 16 (Pharyngeal Residue) scores. Results: Majority of swallows (1,165/1,528; 76.2%) had residue scores of zero. Residue presence (%C2–4 2 > 0) was influenced by age (more in older [ F = 9.908, p = .002]), gender (more in males [ F = 18.70, p < .001]), viscosity (more in pudding, nectar, and honey [ F = 25.30, p < .001]), and volume (more for cup sip [ F = 37.430, p < .001]). When residue was present (363/1,528 = 23.8%), amounts were low ( M = 1% of C2–4 2 , SD = 2.4), and only increasing age was associated with increased residue ( F = 9.008, p = .007) when controlling for gender and swallow task. Increasing residue was incremental (0.01% of C2–4 2 per year). As ASPEKT total residue values increased, MBSImP Component 16 scores also increased. Conclusions: Pharyngeal residue amounts were very low in healthy adults. Residue presence can be influenced by age, gender, and swallow task. However, when present, the amount of pharyngeal residue was only associated with increasing age. Supplemental Material: https://doi.org/10.23641/asha.21957221
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".